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Categories and Subject Descriptors: I.4.9 : Applications I.5.4 : Applications General Terms: Algorithms, Performance, Security Additional Key Words and Phrases: Pose-invariant face recognition, pose-robust feature, multiview learning, face synthesis, survey ACM Reference Format: Changxing Ding Moreover, promising directions for future research are discussed. The motivations, strategies, pros/cons, and performance of representative approaches are described and compared. Existing PIFR methods can be grouped into four categories, that is, pose-robust feature extraction approaches, multiview subspace learning approaches, face synthesis approaches, and hybrid approaches.
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In this article, we discuss the inherent difficulties in PIFR and present a comprehensive review of established techniques.
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However, PIFR is crucial to realizing the full potential of face recognition for real-world applications, since face recognition is intrinsically a passive biometric technology for recognizing uncooperative subjects. Compared to frontal face recognition, which has been intensively studied and has gradually matured in the past few decades, Pose-Invariant Face Recognition (PIFR) remains a largely unsolved problem. A Comprehensive Survey on Pose-Invariant Face Recognition A Comprehensive Survey on Pose-Invariant Face RecognitionĪ Comprehensive Survey on Pose-Invariant Face Recognition CHANGXING DING and DACHENG TAO, University of Technology Sydney The capacity to recognize faces under varied poses is a fundamental human ability that presents a unique challenge for computer vision systems.